TofuBofu AI Visibility

Free AI visibility scan for any B2B company: see how often AI engines recommend it, plus the fixes.

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
100%
Vollständigkeit des Schemas
85%
Qualität der Benennung
90%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~1,314Tokens (Tool-Definitionen)
~5.4 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (1.03% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

{
  "mcpServers": {
    "ai-visibility": {
      "url": "https://tofubofu.com/mcp"
    }
  }
}

Remote-Endpunkte

https://tofubofu.com/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (2)

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🟢scan_ai_visibility(domain, email, geo_scope, locations, competitors, ...)

Start a free AI-visibility scan for a B2B company's website. Checks how often AI engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Mode, Microsoft Copilot) name the company when buyers ask for vendor recommendations, and finds the gaps. The scan runs in the background (roughly 1-2 minutes); call get_visibility_report with the returned report_id to read the score and findings. ASK THE USER for geo_scope and sells_to before calling, if you do not already know them. Everything past `email` is optional and the scan runs without it, but geo_scope changes EVERY question we generate: a firm that sells across one country, scored on one city's questions, looks invisible when it is not. Guessing is worse than asking, and asking costs one line of conversation. Where you do not know, omit the field rather than inventing a plausible value: an omitted field is recorded as unknown, and the report says its framing was assumed. Every parameter carries its own description, generated from the one intake contract the in-app scan form renders from, so what you are told here and what a customer is asked are the same question. Returns: report_id, a report_url to view live, whether an existing report was reused (free scan already used this month), and which intake answers were missing, so you can offer to re-run with them.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "domain": {
      "description": "The company's website or domain, e.g. \"acme.com\".",
      "title": "Domain",
      "type": "string"
    },
    "email": {
      "description": "The user's work email. Required: we send the finished report there and it identifies the account. One free scan per email per month.",
      "title": "Email",
      "type": "string"
    },
    "geo_scope": {
      "default": "",
      "description": "Where do you sell? Getting this wrong skews the whole report: a national firm scored on one city's queries looks invisible, and a local one scored nationally looks unwinnable. One of: global (Anywhere); national (Across one country); national_local (National, with a local angle); local (My own city or region). Shapes every buying query. This is the single highest-leverage answer here. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.",
      "title": "Geo Scope",
      "type": "string"
    },
    "locations": {
      "anyOf": [
        {
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Which places? The country you sell across, or the cities you are bound to. Shapes which geography goes into a localized buying query. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.",
      "title": "Locations"
    },
    "competitors": {
      "anyOf": [
        {
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Who would a buyer otherwise pick? Companies the user says a buyer would pick instead of them. We track whoever the engines name either way, so this is the user's own view rather than the whole comparison set. Shapes the comparison set, the mismatch between who you name and who the engines do, and which of those two a given finding is about. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.",
      "title": "Competitors"
    },
    "buyer_questions": {
      "anyOf": [
        {
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Queries your buyers ask. Queries this company's buyers actually ask. They are put to the engines verbatim and tracked scan over scan, which is what makes a trend line mean anything. Shapes the queries themselves, pinned ahead of the generated ones. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.",
      "title": "Buyer Questions"
    },
    "sells_to": {
      "anyOf": [
        {
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "What size and type of customer? SMB and enterprise are different queries with different winners. Shapes the same. 'Best sales engagement platform for SMB teams' is not the same query as 'for enterprise revenue orgs'. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.",
      "title": "Sells To"
    },
    "capacity": {
      "default": "",
      "description": "Who will do the fixes? We size and sequence the plan to match. A solo founder does not get a list built for a five-person team. One of: solo (Just me); one_marketer (One marketer); small_team (A small team, 2 to 4); full_team (A full team, 5 or more). Shapes the fix plan, which is generated in the tail after the engines answer. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.",
      "title": "Capacity",
      "type": "string"
    }
  },
  "required": [
    "domain",
    "email"
  ],
  "title": "scan_ai_visibilityArguments"
}
🟢get_visibility_report(report_id)

Fetch the results of an AI-visibility scan started with scan_ai_visibility. Args: report_id: The id returned by scan_ai_visibility. Returns: While running: {status: "running", progress}. When done: the visibility score, how often AI mentions the brand, share of voice, top competitors winning the answers, and the highest-priority fixes, plus the report_url.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "report_id": {
      "title": "Report Id",
      "type": "string"
    }
  },
  "required": [
    "report_id"
  ],
  "title": "get_visibility_reportArguments"
}

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